Air pollution represents an increasing threat to the sustainable development of our society. As polluted gases are generated and emitted from various and dynamic sources, it is challenging, costly, and sometimes impossible, to monitor and measure the type and degree of air pollution using existing approaches that rely on on-site detection and/or in-lab tests.
In events of emergency, such as a gas explosion, it is vital to be able to get accurate information on the gaseous constituents, concentration, and distribution in short time (such as in seconds), from safe distance (such as 10 miles in warfare), and without false alarm. None of existing remote sensing/detection techniques can offer such needed capabilities simultaneously, in terms of low false alarm, high sensitivity, long distance, and high spatial resolution.
Existing radio detection and ranging (RADAR) (or light detection and ranging, i.e., LiDAR) is based on radio waves (or light waves) reflected off the object surfaces. They are thus incapable of or inefficient in detecting objects with absorptive surfaces, such as stealth aircrafts. In addition, while LiDAR offers high spatial resolution, the detection range is limited because the light reflection is in general not directed, so that the returning light signal drops quadratically as the range increases.
Raman-based LiDAR systems need to use high power laser to create frequency-shifted signals. However, because the spontaneous Raman scattering process is not directional, the returning signal drops quadratically with the detection distance. As a result, the application range of LiDAR is limited.